Neural Architecture Search (NAS) [1]}, [2]}, [3]}, [4]}, a new paradigm of assembling models automatically, has the potential to bridge the gap. Modern NAS usually aims at designing a family of models for different accuracy-speed trade-offs for different use cases. Because of the high cost associated with searching for the entire family of models, model scaling is commonly used to achieve this goal by scaling [5]}, [6]} up from a base model to form a model family. However, on specialized DC accelerators the fast-widening gap remains even with NAS and model scaling, because they do not have sufficient visibility into hardware architecture details and thus cannot design optimal model families for them.
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